An AzTEC 1.1 mm survey of the GOODS-N field - II. Multiwavelength identifications and redshift distribution
Bibliographic record
Abstract
We present results from a multiwavelength study of 29 sources (false detection probabilities <5 per cent) from a survey of the Great Observatories Origins Deep Survey-North (GOODS-N) field at 1.1 mm using the Astronomical Thermal Emission Camera (AzTEC). Comparing with existing 850 μm Submillimetre Common-User Bolometer Array (SCUBA) studies in the field, we examine differences in the source populations selected at the two wavelengths. The AzTEC observations uniformly cover the entire survey field to a 1σ depth of ∼1 mJy. Searching deep 1.4 GHz Very Large Array (VLA) and Spitzer 3–24 μm catalogues, we identify robust counterparts for 21 1.1 mm sources, and tentative associations for the remaining objects. The redshift distribution of AzTEC sources is inferred from available spectroscopic and photometric redshifts. We find a median redshift of z= 2.7, somewhat higher than z= 2.0 for 850 μm selected sources in the same field, and our lowest redshift identification lies at a spectroscopic redshift z= 1.1460. We measure the 850 μm to 1.1 mm colour of our sources and do not find evidence for ‘850 μm dropouts’, which can be explained by the low signal-to-noise ratio of the observations. We also combine these observed colours with spectroscopic redshifts to derive the range of dust temperatures T, and dust emissivity indices β for the sample, concluding that existing estimates T∼ 30 K and β∼ 1.75 are consistent with these new data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".